1. Review of AI Professor: Liqing Zhang
Contact Information:
Tel:

2. Chapter 1 Basic concepts: What is AI?
Comparison between Human Intelligence and AI
Milestones of AI

3. Chapter 2: Stimulus-Response Agents
2.1 Perception and Action
Perception
Action
Boolean Algebra
Clauses and Forms of Boolean Functions
2.2 Representing and Implementing Action Functions
Production Systems
Networks
The Subsumption Architecture

4. Chapter 2: Stimulus-Response Agents
Neural network
Network of TLUs
TLUs are thought to be simple models of biological neurons
Connection weights
Threshold value

5. Chapter 3: Neural Networks
3.2 Training Single TLUs
Gradient Descent
Widrow-Hoff Rule
Generalized Delta Procedure
3.3 Neural Networks
The Backpropagation Method
Derivation of the Backpropagation Learning Rule
3.4 Generalization, Accuracy, and Overfitting

6. Overfitting

7. Chapter 4: Machine Evolution
Genetic Algorithm
Concept
Genetic Programming
How to define genetic operations and fitness function

8. Chapter 5: State Machines
Concept of State Machine vs Stimulus-Response Agents

9. Chapter 6: Robot Vision
Averaging
Edge enhancement

10. Chapter 8: Uninformed Search
Search Space Graphs
Depth-First Search
Breadth-First Search
Iterative Deepening

11. Chapter 9 : Heuristic Search
Using Evaluation Functions
A General Graph-Searching Algorithm
Algorithm A*
Admissibility
Consistency
Iterative-Deepening A*
Heuristic Functions and Search Efficiency

12. Chapter 10 Planning, Acting, and Learning
X The Sense/Plan/Act Cycle
X Approximate Search
Learning Heuristic Functions
Rewards Instead of Goals

13. Chapter 11:
Not required

14. Chapter 12: Adversarial Search
Minimax Procedure
The Alpha-Beta Procedure
Games of Chance
Learning Evaluation Functions
Not required

15. Chapter 13, 14, 15, 16 The Propositional Calculus
Resolution in the Propositional Calculus
The Predicate Calculus
Resolution in the Predicate Calculus

16. Chapter 17: Knowledge-Based Systems
Confronting the Real World
Reasoning Using Horn Clauses
Maintenance in Dynamic Knowledge Bases
Rule-Based Expert Systems
Rule Learning

17. Rule Extraction

18. Initialize Generic Separate-and-conquer algorithm (GSCA) Initialize
Initialize empty set of rules
repeat the outer loop adds rules until covers all (or most) of the positive instances
Repeat the inner loop adds atoms to until covers only (or mainly) positive instances
Select an atom to add to This is a nondeterministic choice point that can be used for backtracking.
Until covers only (or mainly) positive instances in
We add the rule to the set of rules.
(the positive instances in covered by )
Until covers all (or most) of positive instance in

19. Chapter 18. Representing commonsense Knowledge
Not Required

20. Chapter 19. Reasoning with Uncertain Information
Probabilistic Inference
Bayes Networks
Patterns of Inference in Bayes Networks
Uncertain Evidence
D-Separation
Probabilistic Inference in Polytrees

21. Example

22. The rest chapters are not required